Interpreting nominal compounds for information retrieval
Information Processing and Management: an International Journal - Special issue on natural language processing and information retrieval
Automatic hypertext link typing
Proceedings of the the seventh ACM conference on Hypertext
What the query told the link: the integration of hypertext and information retrieval
HYPERTEXT '97 Proceedings of the eighth ACM conference on Hypertext
Semiautomatic generation of glossary links: a practical solution
Proceedings of the tenth ACM Conference on Hypertext and hypermedia : returning to our diverse roots: returning to our diverse roots
Information retrieval on the web
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Building effective queries in natural language information retrieval
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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Current efforts on the semantic web are mainly focused on the creation of recommendations and standards for adding semantic descriptions to web resources. This situation represents a huge challenge to content creators that have to construct manually such descriptions, implying high costs in material and human resources. This paper presents a multi-agent system that automates partially this task, i.e. the authoring of web documents, reducing content creators labor. This system automatically extracts descriptive information from a set of documents in Spanish language, and constructs two output (web) document collections from them. The first collection is a set of meta-information descriptions based on the Dublin Core specifications. The second output is a collection of XHTML documents for human visualizing and browsing. In order to build the two output collections, the proposed multi-agent system applies several intelligent text processing approaches. This paper describes these approaches, as well as, the methodology used to encode the extracted metadata. It also reports results from processing three document collections of about 45 MB of text, including their associated resources - descriptions and hypertext - generated by the system.